Build Scalable Data Pipelines
Budget: $15 – $25 USD
I’m looking for a data engineer who can design and implement a production-ready data pipeline from the ground up. The goal is simple: move raw data from its assorted sources into an analytics-friendly destination automatically, reliably, and with clear visibility into every step.
Here’s what I need from you:
• A brief architecture plan that explains each stage—from ingestion through transformation to storage—and the rationale behind your choices.
• Clean, well-documented code (Python, SQL, or another language you recommend) checked into a Git repository I can access.
• Automated scheduling, error handling, and monitoring so I can trust the flow to run hands-free.
• A concise deployment guide that lets me recreate the pipeline in a fresh environment without guesswork.
Whether the data originates from APIs, relational databases, flat files, or a mix of all three, I’m flexible on tooling as long as the final solution is maintainable and easy to extend. If you have strong opinions on Spark, Kafka, Glue, or any other framework, feel free to explain why it fits—solid reasoning matters more to me than any specific badge.
Success for this project is a pipeline that loads sample data end-to-end, surfaces meaningful logs, and can be triggered on a schedule I define. If this sounds like a challenge you’d enjoy, please outline your approach, highlight one similar project you’ve delivered, and let’s get started.
Here’s what I need from you:
• A brief architecture plan that explains each stage—from ingestion through transformation to storage—and the rationale behind your choices.
• Clean, well-documented code (Python, SQL, or another language you recommend) checked into a Git repository I can access.
• Automated scheduling, error handling, and monitoring so I can trust the flow to run hands-free.
• A concise deployment guide that lets me recreate the pipeline in a fresh environment without guesswork.
Whether the data originates from APIs, relational databases, flat files, or a mix of all three, I’m flexible on tooling as long as the final solution is maintainable and easy to extend. If you have strong opinions on Spark, Kafka, Glue, or any other framework, feel free to explain why it fits—solid reasoning matters more to me than any specific badge.
Success for this project is a pipeline that loads sample data end-to-end, surfaces meaningful logs, and can be triggered on a schedule I define. If this sounds like a challenge you’d enjoy, please outline your approach, highlight one similar project you’ve delivered, and let’s get started.